Unlike variational circuit learning [1]} that requires the number of model parameters in the order of the training set size, quantum kernel learning (QKL) [2]} can be adopted for limited training scenarios. QKL builds a quantum mapping, \(\phi \) , transforming a classical input vector \(\textbf {x}\)  into a quantum state \(\vert \phi (\textbf {x})\rangle \)  in a quantum Hilbert space.
